<Section title="How this entry is scoped">The captured material assigned to this entry centers on prompt engineering — injecting domain knowledge into AI systems, building eval loops, and structuring prompts and custom GPT workflows. Within that captured material, the term large action models does not appear in any passage that defines or explains it, so this entry cannot responsibly attach a specific meaning to it from the available sources.</Section><Section title="What the surrounding material does establish">If you are exploring how this channel talks about capable, action-oriented AI systems, the closest grounded concepts in the captured material are:</Section>- Prompting as a discipline: detailing your objective is prompting, and the practice is structured around a triangle of primitives — prompt, eval, and dataset.- Workflows over mega-prompts: routing each request to a small, single-purpose prompt in a DAG/graph workflow, rather than one giant prompt.- Natural language as a programming layer: prompts sit atop binary → assembly → C/C++ → high-level languages → scripts as a new natural-language layer of programming.<KeyPoints>- The captured material does not define large action models; no supported definition can be drawn from the assigned sources.- The related grounded material covers prompt engineering, eval loops, and workflow/routing structures for steering AI systems.</KeyPoints>